Estimates of global mortality burden associated with short-term exposure to fine particulate matter (PM2·5)
Why this work is in the frame
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Bibliographic record
Abstract
Background The acute health effects of short-term (hours to days) exposure to fine particulate matter (PM 2·5 ) have been well documented; however, the global mortality burden attributable to this exposure has not been estimated. We aimed to estimate the global, regional, and urban mortality burden associated with short-term exposure to PM 2·5 and the spatiotemporal variations in this burden from 2000 to 2019. Methods We combined estimated global daily PM 2·5 concentrations, annual population counts, country-level mortality rates, and epidemiologically derived exposure–response functions to estimate the mortality attributable to short-term PM 2·5 exposure from 2000 to 2019, in the continental regions and in 13 189 urban centres worldwide at a spatial resolution of 0·1° × 0·1°. We tested the robustness of our mortality estimates with different theoretical minimum risk exposure levels, lag effects, and exposure–response functions. Findings Approximately 1 million (95% CI 690 000–1·3 million) premature deaths per year from 2000 to 2019 were attributable to short-term PM 2·5 exposure, representing 2·08% (1·41–2·75) of total global deaths or 17 (11–22) premature deaths per 100 000 population. Annually, 0·23 million (0·15 million–0·30 million) deaths attributable to short-term PM 2·5 exposure were in urban areas, constituting 22·74% of the total global deaths attributable to this cause and accounting for 2·30% (1·56–3·05) of total global deaths in urban areas. The sensitivity analyses showed that our worldwide estimates of mortality attributed to short-term PM 2·5 exposure were robust. Interpretation Short-term exposure to PM 2·5 contributes a substantial global mortality burden, particularly in Asia and Africa, as well as in global urban areas. Our results highlight the importance of mitigation strategies to reduce short-term exposure to air pollution and its adverse effects on human health. Funding Australian Research Council and the Australian National Health and Medical Research Council.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it